# -*- coding: utf-8 -*-
"""
Created on Mon Dec 30 11:22:58 2019

@author: JimmyMo
"""

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt

rng = numpy.random
learning_rate = 0.01 
trainning_epochs = 1000 
display_step = 50

# Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])

n_samples = train_X.shape[0] 

# tf Graph Input
X = tf.compat.v1.placeholder("float")
Y = tf.compat.v1.placeholder("float")
#
W = tf.Variable(initial_value=rng.randn(), name="weight")
b = tf.Variable(initial_value=rng.randn(), name="bias")



# A = X * W + b
# Construct a linear model
activation = tf.add(tf.multiply(X, W), b) 
# Mean squared error
cost = tf.reduce_sum(input_tensor=tf.pow(activation-Y, 2))/(2 * n_samples) #L2 loss


optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# Initialize the variables (i.e. assign their default value) 
init = tf.compat.v1.global_variables_initializer()

print("begin")

with tf.compat.v1.Session() as sess:
    sess.run(init)
    # Fit all training data
    plt.plot(train_X, train_Y, 'ro', label = 'origin')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    for epoch in range(trainning_epochs):

        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict = {X: x, Y: y})
            #display logs per epoch step
            if (epoch + 1) % display_step == 0:
                c = sess.run(cost, feed_dict = {X: train_X, Y: train_Y})
                #print ("Epoch:", '%04d' % (epoch+1), "cost=", '%.5f' % c, \
                #"W=", sess.run(W), "b=", sess.run(b))
        
        #training_cost = sess.run(cost, feed_dict = {X: train_X, Y: train_Y})
        #print ("Train cost = ", training_cost, "w = ", sess.run(W), "b = ", sess.run(b), '\n')
        #Graphic display
    plt.plot(train_X, train_Y, 'ro', label='Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()
